In the Business Intelligence (BI) computer applications domain, business decision makers use analytical software to pose operational performance questions as queries against multi-dimensionally modeled business databases and data warehouses. These multi-dimensional models and analysis software tools are based on Online Analytic Processing (OLAP) concepts and technology. The analysis activity typically involves the creation and manipulation of a cross-tabular (also called “cross-tab”) and/or graphical presentation of the data.
Large OLAP databases and multi-dimensionally modeled data warehouses typically contain large numbers of dimensional members or flat/non-existent dimensional hierarchies, or both. This is due to a variety of factors, including the volume of available and important data as a business operates and grows, the time constraints and computing resources required to stage and model the data warehouse and make it available for business decision-making processes, the need for flexible, unconstrained models for key business dimensions such as Customers and Time, or non-hierarchical models for inherently parent-child-relationship dimensions such as Invoices and Orders.
Multidimensional queries posed in this “large-OLAP” context often yield either a sparse results matrix due to the absence of data for many dimensional intersections as the number of dimensions or the number of members increases, or a large number of uninteresting children members, and sometimes both.
The problem is exacerbated by the physical display limits and two-dimensional nature of a computer monitor. Such an outcome makes it more difficult to compare relevant members, due to the ‘distance’ between them, either within the dimensional model or physically on the display.
Given these characteristics of large OLAP, it is often difficult to compare members from multiple different dimensions in the context of one or more additional dimensions on the opposite edge of a cross-tab. A user often needs to create separate views or reports for each of the members from multiple different dimensions, and compare them by switching between views or printed output of the report pages.
It is often difficult to compare members from different hierarchies of the same dimension in the context of one or more additional dimensions on the opposite edge of a cross-tab. Some existing BI tools allow different hierarchies of members from the same dimension to be displayed on opposite axes of the cross-tab. A disadvantage of this approach is that data may only appear for valid cell intersections. The opposite cross-tab axis can involve redundant members, and can no longer be dedicated to supplying additional context from other dimensions.
Where members of a repeating or parent-child hierarchy may have the same display caption but different parent-members, or reside in different hierarchies, it is difficult to discriminate them without additional decoration, e.g., a parent and/or hierarchy prefix. Such elaboration however takes up valuable display real estate, requiring word wrapping or cell resizing techniques and associated user interface fixtures to help manage the display. In some BI tools, user interface tooltips are used to display the fully-qualified path of the dimensional member, e.g., [Time].[2005].[2005 Q 1], or a separate user interface capability is invoked to elaborate additional explanatory details.
In these situations, it is difficult to compare members in the context of an additional dimension, or in the context of more than one measure value at a time. Some existing BI tools allow nesting of members from additional dimensions or multiple measures along one edge of a cross-tab. As the number of members or nesting levels increases, this approach suffers from the physical display limits and display management manipulations previously mentioned above. Some existing BI tools allow intervening members to be hidden or deleted from the cross-tab to improve the display proximity of the members being compared. For a large number of members, this technique can be tedious and time-consuming. Some existing BI tools allow cross-tab axis members to be ‘pinned’ (i.e., prevent them from being scrolled out of view) and ranges of intervening members to be collapsed (i.e., temporarily hidden) to improve the display proximity of the members being compared. Both techniques require numerous non-analytical user interface gestures that detract from the comparison activity. Display management manipulations or the need to print/arrange report outputs distract users from the primary task of comparative member analysis.
It is therefore desirable to provide a mechanism for allowing easy and accurate comparative analysis of BI data in reports.